Simulation-based predictive analytics for dynamic queueing systems

Huiyin Ouyang, Barry L. Nelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Simulation and simulation optimization have primarily been used for static system design problems based on long-run average performance measures. Control or policy-based optimization has been a weakness, because it requires a way to predict future behavior based on current state and time information. This work is a first step in that direction with a focus on congestion measures for queueing systems. The idea is to fit predictive models to dynamic sample paths of the system state from a detailed simulation. We propose a two-step method to dynamically predict the probability that the system state belongs to a certain subset and test the performance of this method on two examples.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1716-1727
Number of pages12
ISBN (Electronic)9781538634288
DOIs
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas
Period12/3/1712/6/17

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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    Ouyang, H., & Nelson, B. L. (2017). Simulation-based predictive analytics for dynamic queueing systems. In V. Chan (Ed.), 2017 Winter Simulation Conference, WSC 2017 (pp. 1716-1727). (Proceedings - Winter Simulation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2017.8247910